Gallicchio, C. (2023). Diversifying Non-dissipative Reservoir Computing Dynamics. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. doi: 10.1007/978-3-031-44198-1_15
ABSTRACT: The Euler State Network (EuSNs) model is a recently proposed Reservoir Computing methodology that provides stable and non-dissipative untrained dynamics by discretizing an appropriately constrained ODE. In this paper, we propose alternative formulations of the reservoirs for EuSNs, aiming at improving the diversity of the resulting dynamics. Our empirical analysis points out the effectiveness of the proposed approaches on a large pool of time-series classification tasks.
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